Innovative in Silico Approaches for Characterization of Genes and Proteins

SCN1A intronic variants impact on Nav1.1 protein expression and sodium channel function, and associated with epilepsy phenotypic severity

High-throughput sequencing has identified numerous intronic variants in the SCN1A gene in epilepsy patients. Abnormal mRNA splicing caused by these variants can lead to significant phenotypic differences, but the mechanisms of epileptogenicity and phenotypic differences remain unknown. Two variants, c.4853-1 G>C and c.4853-25 T>A, were identified in intron 25 of SCN1A, which were associated with severe Dravet syndrome (DS) and mild focal epilepsy with febrile seizures plus (FEFS+), respectively. The impact of these variants on protein expression, electrophysiological properties of sodium channels and their correlation with epilepsy severity was investigated through plasmid construction and transfection based on the aberrant spliced mRNA. We found that the expression of truncated mutant proteins was significantly reduced on the cell membrane, and retained in the cytoplasmic endoplasmic reticulum. The mutants caused a decrease in current density, voltage sensitivity, and an increased vulnerability of channel, leading to a partial impairment of sodium channel function. Notably, the expression of DS-related mutant protein on the cell membrane was higher compared to that of FEFS+-related mutant, whereas the sodium channel function impairment caused by DS-related mutant was comparatively milder than that caused by FEFS+-related mutant. Our study suggests that differences in protein expression levels and altered electrophysiological properties of sodium channels play important roles in the manifestation of diverse epileptic phenotypes. The presence of intronic splice site variants may result in severe phenotypes due to the dominant-negative effects, whereas non-canonical splice site variants leading to haploinsufficiency could potentially cause milder phenotypes.

Network and systems biology approaches help investigate gene regulatory interactions between Salmonella disease and host in chickens: Model-based in silico evidence combined with gene expression assays

BACKGROUND

Salmonella enteritidis (SE), a previously widespread infectious disease, is still cited as a major factor in economic losses in commercial chicken production. The host's genetic immune system determines the pathogenicity of a particular bacterium. To shed light on this topic, it was necessary to understand the key candidate genes essential for regulating susceptibility and resistance to the target disease. The field of poultry farming in particular has benefited greatly from the connection between quantitative and molecular genetics.

OBJECTIVES

This study aims to identify the most important immune-related genes and their signalling pathways (gene ontology, co-expression and interactions) and to analyse their accumulation in host-resistant SE diseases by combining gene expression assays with model-based in silico evidence.

METHODS

A two-step experimental design is followed. To start, we used free computational tools and online bioinformatics resources, including predicting gene function using a multiple association network integration algorithm (geneMania), the Kyoto Encyclopedia of Genes and Genomes, the Annotation, Visualization and Integrated Discovery (DAVID) database and the stimulator of interferon genes. Natural resistance-associated macrophage protein 1 (NRAMP1), Toll-like receptor 4 (TLR4), interferon-γ (IFNγ), immunoglobulin Y (IgY) and interleukin 8 (IL8) were among the five genes whose expression levels in liver, spleen, and cecum were evaluated at 1107 SE after 48 h of inoculation. This molecular study was developed in the second phase of research to validate the in silico observations. Next, we use five promising biomarkers for relative real-time polymerase chain reaction (PCR) quantification: TLR4, IL8, NRAMP1, IFNγ and IgY genes in two case and control assays. The 2-∆∆Ct Livak and Schmittgen method was used to compare the expression of genes in treated and untreated samples. This method normalizes the expression of the target gene to that of actin, an internal control and estimates the change in expression relative to the untreated control. Internal control was provided by the Beta actin gene. Next, statistically, the postdoc test was used for the evaluation of treatments using SAS version 9.4, and p values of 0.05 and 0.01 were chosen for significant level.

RESULTS

Interestingly, the results of our study suggest the involvement of various factors in the host immune response to Salmonella. These include inducible nitric oxide synthase, NRAMP1, immunoglobulin light chain (IgL), transforming growth factor B family (TGFb2, TGFb3 and TGFb4), interleukin 2 (IL2), apoptosis inhibitor protein 1 (IAP1), TLR4, myeloid differentiation protein 2 (MD2), IFNγ, caspase 1 (CASP1), lipopolysaccharide-induced tumour necrosis factor (LITAF), cluster of differentiation 28 (CD28) and prosaposin (PSAP). The summary of gene ontology and related genes found for SE resistance was surprisingly comprehensive and covered the following topics: positive regulation of endopeptidase activity, interleukin-8 production, chemokine production, interferon-gamma production, interleukin-6 production, positive regulation of mononuclear cell proliferation and response to interferon-gamma. The role of these promising biomarkers in our networks against SE susceptibility is essentially confirmed by these results. After 48 h, the spleen showed significant expression of the tissue-specific gene expression patterns for NRAMP1 and IL8 in the cecum, spleen and liver. Based on this information, this report searches for resistance and susceptibility lineages in most genomic regions for SE.

CONCLUSIONS

In conclusion, the development of an appropriate selection program to improve resistance to salmonellosis can be facilitated by a comprehensive understanding of the immune responses of the chicken immune system after SE exposure.

Advancing plant biology through deep learning-powered natural language processing

The application of deep learning methods, specifically the utilization of Large Language Models (LLMs), in the field of plant biology holds significant promise for generating novel knowledge on plant cell systems. The LLM framework exhibits exceptional potential, particularly with the development of Protein Language Models (PLMs), allowing for in-depth analyses of nucleic acid and protein sequences. This analytical capacity facilitates the discernment of intricate patterns and relationships within biological data, encompassing multi-scale information within DNA or protein sequences. The contribution of PLMs extends beyond mere sequence patterns and structure--function recognition; it also supports advancements in genetic improvements for agriculture. The integration of deep learning approaches into the domain of plant sciences offers opportunities for major breakthroughs in basic research across multi-scale plant traits. Consequently, the strategic application of deep learning methodologies, particularly leveraging the potential of LLMs, will undoubtedly play a pivotal role in advancing plant sciences, plant production, plant uses and propelling the trajectory toward sustainable agroecological and agro-food transitions.

Decoding common genetic alterations between Barrett's esophagus and esophageal adenocarcinoma: A bioinformatics analysis

Background

Esophageal adenocarcinoma (EAC) is a common cancer with a poor prognosis in advanced stages. Therefore, early EAC diagnosis and treatment have gained attention in recent decades. It has been found that various pathological changes, particularly Barrett's Esophagus (BE), can occur in the esophageal tissue before the development of EAC. In this study, we aimed to identify the molecular contributor in BE to EAC progression by detecting the essential regulatory genes that are differentially expressed in both BE and EAC.

Materials and methods

We conducted a comprehensive bioinformatics analysis to detect BE and EAC-associated genes. The common differentially expressed genes (DEGs) and common single nucleotide polymorphisms (SNPs) were detected using the GEO and DisGeNET databases, respectively. Then, hub genes and the top modules within the protein-protein interaction network were identified. Moreover, the co-expression network of the top module by the HIPPIE database was constructed. Additionally, the gene regulatory network was constructed based on miRNAs and circRNAs. Lastly, we inspected the DGIdb database for possible interacted drugs.

Results

Our microarray dataset analysis identified 92 common DEGs between BE and EAC with significant enrichment in skin and epidermis development genes. The study also identified 22 common SNPs between BE and EAC. The top module of PPI network analysis included SCEL, KRT6A, SPRR1A, SPRR1B, SPRR3, PPL, SPRR2B, EVPL, and CSTA. We constructed a ceRNA network involving three specific mRNAs, 23 miRNAs, and 101 selected circRNAs. According to the results from the DGIdb database, TD101 was found to interact with the KRT6A gene.

Conclusion

The present study provides novel potential candidate genes that may be involved in the molecular association between Esophageal adenocarcinoma and Barrett's Esophagus, resulting in developing the diagnostic tools and therapeutic targets to prevent progression of BE to EAC.

Entamoeba histolytica: In Silico and In Vitro Oligomerization of EhHSTF5 Enhances Its Binding to the HSE of the EhPgp5 Gene Promoter

Throughout its lifecycle, Entamoeba histolytica encounters a variety of stressful conditions. This parasite possesses Heat Shock Response Elements (HSEs) which are crucial for regulating the expression of various genes, aiding in its adaptation and survival. These HSEs are regulated by Heat Shock Transcription Factors (EhHSTFs). Our research has identified seven such factors in the parasite, designated as EhHSTF1 through to EhHSTF7. Significantly, under heat shock conditions and in the presence of the antiamoebic compound emetine, EhHSTF5, EhHSTF6, and EhHSTF7 show overexpression, highlighting their essential role in gene response to these stressors. Currently, only EhHSTF7 has been confirmed to recognize the HSE as a promoter of the EhPgp5 gene (HSE_EhPgp5), leaving the binding potential of the other EhHSTFs to HSEs yet to be explored. Consequently, our study aimed to examine, both in vitro and in silico, the oligomerization, and binding capabilities of the recombinant EhHSTF5 protein (rEhHSTF5) to HSE_EhPgp5. The in vitro results indicate that the oligomerization of rEhHSTF5 is concentration-dependent, with its dimeric conformation showing a higher affinity for HSE_EhPgp5 than its monomeric state. In silico analysis suggests that the alpha 3 α-helix (α3-helix) of the DNA-binding domain (DBD5) of EhHSTF5 is crucial in binding to the major groove of HSE, primarily through hydrogen bonding and salt-bridge interactions. In summary, our results highlight the importance of oligomerization in enhancing the affinity of rEhHSTF5 for HSE_EhPgp5 and demonstrate its ability to specifically recognize structural motifs within HSE_EhPgp5. These insights significantly contribute to our understanding of one of the potential molecular mechanisms employed by this parasite to efficiently respond to various stressors, thereby enabling successful adaptation and survival within its host environment.

Evidence for the druggability of aldosterone targets in heart failure: A bioinformatics and data science-driven decision-making approach

BACKGROUND

Aldosterone plays a key role in the neurohormonal drive of heart failure. Systematic prioritization of drug targets using bioinformatics and database-driven decision-making can provide a competitive advantage in therapeutic R&D. This study investigated the evidence on the druggability of these aldosterone targets in heart failure.

METHODS

The target disease predictability of mineralocorticoid receptors (MR) and aldosterone synthase (AS) in cardiac failure was evaluated using Open Targets target-disease association scores. The Open Targets database collections were downloaded to MongoDB and queried according to the desired aggregation level, and the results were retrieved from the Europe PMC (data type: text mining), ChEMBL (data type: drugs), Open Targets Genetics Portal (data type: genetic associations), and IMPC (data type: genetic associations) databases. The target tractability of MR and AS in the cardiovascular system was investigated by computing activity scores in a curated ChEMBL database using supervised machine learning.

RESULTS

The medians of the association scores of the MR and AS groups were similar, indicating a comparable predictability of the target disease. The median of the MR activity scores group was significantly lower than that of AS, indicating that AS has higher target tractability than MR [Hodges-Lehmann difference 0.62 (95%CI 0.53-0.70, p < 0.0001]. The cumulative distributions of the overall multiplatform association scores of cardiac diseases with MR were considerably higher than with AS, indicating more advanced investigations on a wider range of disorders evaluated for MR (Kolmogorov-Smirnov D = 0.36, p = 0.0009). In curated ChEMBL, MR had a higher cumulative distribution of activity scores in experimental cardiovascular assays than AS (Kolmogorov-Smirnov D = 0.23, p < 0.0001). Documented clinical trials for MR in heart failures surfaced in database searches, none for AS.

CONCLUSIONS

Although its clinical development has lagged behind that of MR, our findings indicate that AS is a promising therapeutic target for the treatment of cardiac failure. The multiplatform-integrated identification used in this study allowed us to comprehensively explore the available scientific evidence on MR and AS for heart failure therapy.

An Update on Current Antiviral Strategies to Combat Human Cytomegalovirus Infection

Human cytomegalovirus (HCMV) remains an essential global concern due to its distinct life cycle, mutations and latency. As HCMV is a herpesvirus, it establishes a lifelong persistence in the host through a chronic state of infection. Immunocompromised individuals are at risk of significant morbidity and mortality from the virus. Until now, no effective vaccine has been developed to combat HCMV infection. Only a few antivirals targeting the different stages of the virus lifecycle and viral enzymes are licensed to manage the infection. Therefore, there is an urgent need to find alternate strategies to combat the infection and manage drug resistance. This review will provide an insight into the clinical and preclinical antiviral approaches, including HCMV antiviral drugs and nucleic acid-based therapeutics.

Zinc/iron-regulated transporter-like protein gene family in Theobroma cacao L: Characteristics, evolution, function and 3D structure analysis

The zinc/iron-regulated transporter-like protein (ZIP) gene family first identified in plants is highly distributed in the plant kingdom. This family has previously been reported to transport several essential and non-essential cationic elements, including those toxic to many economically important crops such as cacao (Theobroma cacao L.). In this article, we present a detailed study on physicochemical properties, evolution, duplication, gene structure, promoter region and TcZIP family three-dimensional protein structure. A total of 11 TcZIP genes have been identified to encode proteins from 309 to 435 aa, with localization in the plasma membrane and chloroplast, containing 6-9 putative domains (TM). Interspecies phylogenetic analysis subdivided the ZIP proteins into four groups. Segmental duplication events significantly contributed to the expansion of TcZIP genes. These genes underwent high pressure of purifying selection. The three-dimensional structure of the proteins showed that α helix conformations are predominant with several pocket sites, containing the metal binding site, with the residues leucine (LEU), alanine (ALA), glycine (GLY), serine (SER), lysine (LYS) and histidine (HIS) the most predicted. Regarding the analysis of the protein-protein interaction and enrichment of the gene ontology, four biological processes were assigned, the most important being the cation transport. These new discoveries expand the knowledge about the function, evolution, protein structures and interaction of ZIP family proteins in cacao and contribute to develop cacao genotypes enriched with important mineral nutrients as well as genotypes that bioaccumulate or exclude toxic metals.